Steps

I. Reading and processing the New York Times (NYT) state-level COVID-19 data

1. Read in the data

Read in the COVID data with data.table:fread() from the NYT GitHub repository: “https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv”
Read in the state population data with data.table:fread() from the repository: “https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv”"
Merge datasets

cv_states <- as.data.frame(data.table::fread("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv") )
state_pops <- as.data.frame(data.table::fread("https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv"))
state_pops$abb <- state_pops$state
state_pops$state <- state_pops$state_name
state_pops$state_name <- NULL
### FINISH THE CODE HERE ###
cv_states <- merge(cv_states,state_pops, by = "state")

2. Look at the data

Inspect the dimensions, head, and tail of the data
Inspect the structure of each variables. Are they in the correct format?

dim(cv_states)

## [1] 12490     9

head(cv_states)

##     state       date fips  cases deaths geo_id population pop_density abb
## 1 Alabama 2020-08-25    1 117242   2037      1    4887871    96.50939  AL
## 2 Alabama 2020-05-12    1  10464    435      1    4887871    96.50939  AL
## 3 Alabama 2020-09-08    1 133606   2277      1    4887871    96.50939  AL
## 4 Alabama 2020-08-13    1 105557   1890      1    4887871    96.50939  AL
## 5 Alabama 2020-05-25    1  14986    566      1    4887871    96.50939  AL
## 6 Alabama 2020-09-07    1 132973   2276      1    4887871    96.50939  AL

tail(cv_states)

##         state       date fips cases deaths geo_id population pop_density abb
## 12485 Wyoming 2020-05-12   56   675      7     56     577737    5.950611  WY
## 12486 Wyoming 2020-07-17   56  2069     24     56     577737    5.950611  WY
## 12487 Wyoming 2020-09-10   56  4199     42     56     577737    5.950611  WY
## 12488 Wyoming 2020-09-22   56  5016     49     56     577737    5.950611  WY
## 12489 Wyoming 2020-05-26   56   850     13     56     577737    5.950611  WY
## 12490 Wyoming 2020-08-12   56  3086     29     56     577737    5.950611  WY

str(cv_states)

## 'data.frame':    12490 obs. of  9 variables:
##  $ state      : chr  "Alabama" "Alabama" "Alabama" "Alabama" ...
##  $ date       : chr  "2020-08-25" "2020-05-12" "2020-09-08" "2020-08-13" ...
##  $ fips       : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ cases      : int  117242 10464 133606 105557 14986 132973 116710 38045 159169 30021 ...
##  $ deaths     : int  2037 435 2277 1890 566 2276 2024 950 2558 839 ...
##  $ geo_id     : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ population : int  4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ...
##  $ pop_density: num  96.5 96.5 96.5 96.5 96.5 ...
##  $ abb        : chr  "AL" "AL" "AL" "AL" ...

3. Format the data

Make date into a date variable
Make state and abb into a factor variable
Order the data first by state, second by date
Confirm the variables are now correctly formatted
Inspect the range values for each variable. What is the date range? The range of cases and deaths?

# format the date
cv_states$date <- as.Date(cv_states$date, format="%Y-%m-%d")
# format the state variable
state_list <- unique(cv_states$state)
cv_states$state <- factor(cv_states$state, levels = state_list)
# format the state abbreviation (abb) variable
abb_list = unique(cv_states$abb)
cv_states$abb = factor(cv_states$abb, levels = abb_list)
  
# order the data first by state, second by date
cv_states = cv_states[order(cv_states$state, cv_states$date),]
# Confirm the variables are now correctly formatted
str(cv_states)

## 'data.frame':    12490 obs. of  9 variables:
##  $ state      : Factor w/ 52 levels "Alabama","Alaska",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ date       : Date, format: "2020-03-13" "2020-03-14" ...
##  $ fips       : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ cases      : int  6 12 23 29 39 51 78 106 131 157 ...
##  $ deaths     : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ geo_id     : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ population : int  4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ...
##  $ pop_density: num  96.5 96.5 96.5 96.5 96.5 ...
##  $ abb        : Factor w/ 52 levels "AL","AK","AZ",..: 1 1 1 1 1 1 1 1 1 1 ...

head(cv_states)

##       state       date fips cases deaths geo_id population pop_density abb
## 212 Alabama 2020-03-13    1     6      0      1    4887871    96.50939  AL
## 224 Alabama 2020-03-14    1    12      0      1    4887871    96.50939  AL
## 219 Alabama 2020-03-15    1    23      0      1    4887871    96.50939  AL
## 143 Alabama 2020-03-16    1    29      0      1    4887871    96.50939  AL
## 187 Alabama 2020-03-17    1    39      0      1    4887871    96.50939  AL
## 49  Alabama 2020-03-18    1    51      0      1    4887871    96.50939  AL

tail(cv_states)

##         state       date fips cases deaths geo_id population pop_density abb
## 12357 Wyoming 2020-10-22   56 10119     68     56     577737    5.950611  WY
## 12476 Wyoming 2020-10-23   56 10545     68     56     577737    5.950611  WY
## 12345 Wyoming 2020-10-24   56 10805     68     56     577737    5.950611  WY
## 12400 Wyoming 2020-10-25   56 11041     68     56     577737    5.950611  WY
## 12334 Wyoming 2020-10-26   56 11477     77     56     577737    5.950611  WY
## 12457 Wyoming 2020-10-27   56 11806     77     56     577737    5.950611  WY

# Inspect the range values for each variable. What is the date range? The range of cases and deaths?
head(cv_states)

##       state       date fips cases deaths geo_id population pop_density abb
## 212 Alabama 2020-03-13    1     6      0      1    4887871    96.50939  AL
## 224 Alabama 2020-03-14    1    12      0      1    4887871    96.50939  AL
## 219 Alabama 2020-03-15    1    23      0      1    4887871    96.50939  AL
## 143 Alabama 2020-03-16    1    29      0      1    4887871    96.50939  AL
## 187 Alabama 2020-03-17    1    39      0      1    4887871    96.50939  AL
## 49  Alabama 2020-03-18    1    51      0      1    4887871    96.50939  AL

summary(cv_states)

##            state            date                 fips           cases       
##  Washington   :  281   Min.   :2020-01-21   Min.   : 1.00   Min.   :     1  
##  Illinois     :  278   1st Qu.:2020-04-30   1st Qu.:16.00   1st Qu.:  3294  
##  California   :  277   Median :2020-06-29   Median :29.00   Median : 20016  
##  Arizona      :  276   Mean   :2020-06-29   Mean   :29.77   Mean   : 66099  
##  Massachusetts:  270   3rd Qu.:2020-08-28   3rd Qu.:44.00   3rd Qu.: 76358  
##  Wisconsin    :  266   Max.   :2020-10-27   Max.   :72.00   Max.   :923828  
##  (Other)      :10842                                                        
##      deaths          geo_id        population        pop_density       
##  Min.   :    0   Min.   : 1.00   Min.   :  577737   Min.   :    1.292  
##  1st Qu.:   75   1st Qu.:16.00   1st Qu.: 1805832   1st Qu.:   54.956  
##  Median :  528   Median :29.00   Median : 4468402   Median :  107.860  
##  Mean   : 2282   Mean   :29.77   Mean   : 6561680   Mean   :  420.670  
##  3rd Qu.: 2238   3rd Qu.:44.00   3rd Qu.: 7535591   3rd Qu.:  229.511  
##  Max.   :33092   Max.   :72.00   Max.   :39557045   Max.   :11490.120  
##                                                     NA's   :229        
##       abb       
##  WA     :  281  
##  IL     :  278  
##  CA     :  277  
##  AZ     :  276  
##  MA     :  270  
##  WI     :  266  
##  (Other):10842

min(cv_states$date)

## [1] "2020-01-21"

max(cv_states$date)

## [1] "2020-10-27"

4. Add `new_cases` and `new_deaths` and correct outliers

Add variables for new cases, new_cases, and new deaths, new_deaths:
- Hint: new_cases is equal to the difference between cases on date i and date i-1, starting on date i=2
Use plotly for EDA: See if there are outliers or values that don’t make sense for new_cases and new_deaths. Which states and which dates have strange values?
Correct outliers: Set negative values for new_cases or new_deaths to 0
Recalculate cases and deaths as cumulative sum of updates new_cases and new_deaths

# Add variables for new_cases and new_deaths:
for (i in 1:length(state_list)) {
  cv_subset = subset(cv_states, state == state_list[i])
  cv_subset = cv_subset[order(cv_subset$date),]
  # add starting level for new cases and deaths
  cv_subset$new_cases = cv_subset$cases[1]
  cv_subset$new_deaths = cv_subset$deaths[1]
  #### FINISH THE CODE HERE ###
  for (j in 2:nrow(cv_subset)) {
    cv_subset$new_cases[j] =  cv_subset$cases[j] - cv_subset$cases[j-1]
    cv_subset$new_deaths[j] = cv_subset$deaths[j] - cv_subset$deaths[j-1]
  }
  # include in main dataset
  cv_states$new_cases[cv_states$state==state_list[i]] = cv_subset$new_cases
  cv_states$new_deaths[cv_states$state==state_list[i]] = cv_subset$new_deaths
}
# Inspect outliers in new_cases and new_deaths using plotly
### FINISH THE CODE HERE ###
p1<-ggplot(cv_states, 
           aes( x=date, y=new_cases, color=state )
           ) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p1)

p1<-NULL 
### FINISH THE CODE HERE ###
p2<-ggplot(cv_states, 
           aes(x=date, y=new_deaths, color=state)
           ) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p2)

p2<-NULL

# set negative new case or death counts to 0
cv_states$new_cases[cv_states$new_cases<0] = 0
cv_states$new_deaths[cv_states$new_deaths<0] = 0
# Recalculate `cases` and `deaths` as cumulative sum of updates `new_cases` and `new_deaths`
for (i in 1:length(state_list)) {
  cv_subset = subset(cv_states, state == state_list[i])
  # add starting level for new cases and deaths
  cv_subset$cases = cv_subset$cases[1]
  cv_subset$deaths = cv_subset$deaths[1]
  for (j in 2:nrow(cv_subset)) {
    cv_subset$cases[j] = cv_subset$new_cases[j] + cv_subset$cases[j-1]
    cv_subset$deaths[j] = cv_subset$new_deaths[j] + cv_subset$deaths[j-1]
  }
  # include in main dataset
  cv_states$cases[cv_states$state==state_list[i]] = cv_subset$cases
  cv_states$deaths[cv_states$state==state_list[i]] = cv_subset$deaths
}

5. Add additional variables

Add population-normalized (by 100,000) variables for each variable type (rounded to 1 decimal place). Make sure the variables you calculate are in the correct format (numeric). You can use the following variable names:
- per100k = cases per 100,000 population
- newper100k= new cases per 100,000
- deathsper100k = deaths per 100,000
- newdeathsper100k = new deaths per 100,000
Add a “naive CFR” variable representing deaths / cases on each date for each state
Create a dataframe representing values on the most recent date, cv_states_today, as done in lecture

# add population normalized (by 100,000) counts for each variable
cv_states$per100k =  as.numeric(format(round(cv_states$cases/(cv_states$population/100000),1),nsmall=1))
cv_states$newper100k =  as.numeric(format(round(cv_states$new_cases/(cv_states$population/100000),1),nsmall=1))
cv_states$deathsper100k =  as.numeric(format(round(cv_states$deaths/(cv_states$population/100000),1),nsmall=1))
cv_states$newdeathsper100k =  as.numeric(format(round(cv_states$new_deaths/(cv_states$population/100000),1),nsmall=1))
# add a naive_CFR variable = deaths / cases
cv_states = cv_states %>% mutate(naive_CFR = round((deaths*100/cases),2))
# create a `cv_states_today` variable
### FINISH THE CODE HERE ###
max_date = max(cv_states$date)
cv_states_today = cv_states %>% filter(date==as.Date(max_date))

II. Interactive plots

6. Explore scatterplots using `plot_ly()`

Create a scatterplot using plot_ly() representing pop_density vs. various variables (e.g. cases, per100k, deaths, deathsper100k) for each state on most recent date (cv_states_today)
- Use hover to identify any outliers.
- Remove those outliers and replot.
Choose one plot. For this plot:
- Add hoverinfo specifying the state name, cases per 100k, and deaths per 100k, similarly to how we did this in the lecture notes
- Add layout information to title the chart and the axes
- Enable hovermode = "compare"

# pop_density vs. cases
### FINISH THE CODE HERE ###
cv_states_today %>% 
  plot_ly(x = ~pop_density, y = ~cases, 
          type = 'scatter', mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))

# filter out "District of Columbia"
cv_states_today_scatter <- cv_states_today %>% filter(state!="District of Columbia")
# pop_density vs. cases after filtering
cv_states_today_scatter %>% filter(state!="District of Columbia") %>% 
  plot_ly(x = ~pop_density, y = ~cases, 
          type = 'scatter', mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))

# pop_density vs. deathsper100k
cv_states_today_scatter %>% filter(state!="District of Columbia") %>% 
  plot_ly(x = ~pop_density, y = ~deathsper100k, 
          type = 'scatter', mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))

# Adding hoverinfo
cv_states_today_scatter %>% 
  plot_ly(x = ~pop_density, y = ~deathsper100k,
          type = 'scatter', mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5),
          hoverinfo = 'text',
          text = ~paste( paste(state, ":", sep=""), paste(" Cases per 100k: ", per100k, sep="") , paste(" Deaths per 100k: ",
                        deathsper100k, sep=""), sep = "<br>")) %>%
  layout(title = "Population-normalized COVID-19 deaths (per 100k) vs. population density for US states",
                  yaxis = list(title = "Deaths per 100k"), xaxis = list(title = "Population Density"),
         hovermode = "compare")

7. Explore scatterplot trend interactively using `ggplotly()` and `geom_smooth()`

For pop_density vs. newdeathsper100k create a chart with the same variables using gglot_ly()
- What’s the geom_*() we need here?
Explore the pattern between \(x\) and \(y\) using geom_smooth()
- Explain what you see. Do you think pop_density is a correlate of newdeathsper100k?

### FINISH THE CODE HERE ###
p <- ggplot(cv_states_today_scatter, aes(x=pop_density, y=deathsper100k, size=population)) + geom_smooth() + geom_point()
ggplotly(p)

8. Multiple line chart

Create a line chart of the naive_CFR for all states over time using plot_ly()
- Use hoverinfo to identify states that had a “first peak”
- Use the zoom and pan tools to inspect the naive_CFR for the states that had a “first peak” in September. How have they changed over time?
Create one more line chart, for Texas only, which shows new_cases and new_deaths together in one plot. Hint: use add_lines()
- Use hoverinfo to “eyeball” the approximate peak of deaths and peak of cases. What is the time delay between the peak of cases and the peak of deaths?

# Line chart for naive_CFR for all states over time using `plot_ly()`
plot_ly(cv_states, x = ~date, y = ~naive_CFR, color = ~state, type = "scatter", mode = "lines")

# Line chart for Texas showing new_cases and new_deaths together
### FINISH THE CODE HERE ###
cv_states %>% filter(state=="Texas") %>% plot_ly(x = ~date, y = ~new_cases, type = "scatter", mode = "lines") %>% add_lines(x = ~date, y = ~new_deaths, type = "scatter", mode = "lines")

9. Heatmaps

Create a heatmap to visualize new_cases for each state on each date greater than April 1st, 2020 - Start by mapping selected features in the dataframe into a matrix using the tidyr package function pivot_wider(), naming the rows and columns, as done in the lecture notes - Use plot_ly() to create a heatmap out of this matrix - Create a second heatmap in which the pattern of new_cases for each state over time becomes more clear by filtering to only look at dates every two weeks

# Map state, date, and new_cases to a matrix
library(tidyr)
cv_states_mat <- cv_states %>% select(state, date, new_cases) %>% filter(date>as.Date("2020-04-01"))
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = new_cases))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)
# Create a heatmap using plot_ly()
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
             z=~cv_states_mat2,
             type="heatmap",
             showscale=T)

# Create a second heatmap after filtering to only include dates every other week
filter_dates <- seq(as.Date("2020-04-01"), as.Date("2020-10-01"), by="2 weeks")
### FINISH THE CODE HERE ### 
cv_states_mat <- cv_states %>% select(state, date, new_cases) %>% filter( date %in% filter_dates)
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = new_cases))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)
# Create a heatmap using plot_ly()
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
             z=~cv_states_mat2,
             type="heatmap",
             showscale=T)

10. Map

Create a map to visualize the naive_CFR by state on May 1st, 2020
Compare with a map visualizing the naive_CFR by state on most recent date
Plot the two maps side by side using subplot(). Make sure the shading is for the same range of values (google is your friend for this)
Describe the difference in the pattern of the CFR.

### For May 1 2020
# Extract the data for each state by its abbreviation
cv_CFR <- cv_states %>% filter(date=="2020-05-01") %>% select(state, abb, naive_CFR, cases, deaths) # select data
cv_CFR$state_name <- cv_CFR$state
cv_CFR$state <- cv_CFR$abb
cv_CFR$abb <- NULL
# Create hover text
cv_CFR$hover <- with(cv_CFR, paste(state_name, '<br>', "CFR: ", naive_CFR, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths))
# Set up mapping details
set_map_details <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)
# Make sure both maps are on the same color scale
shadeLimit <- 9
# Create the map
fig <- plot_geo(cv_CFR, locationmode = 'USA-states') %>% 
  add_trace(
    z = ~naive_CFR, text = ~hover, locations = ~state,
    color = ~naive_CFR, colors = 'Purples'
  )
fig <- fig %>% colorbar(title = "CFR May 1 2020", color = c(0,shadeLimit))
fig <- fig %>% layout(
    title = paste('CFR by State as of', Sys.Date(), '<br>(Hover for value)'),
    geo = set_map_details
  )
fig_May1 <- fig
#############
### For Today
# Extract the data for each state by its abbreviation
cv_CFR <- cv_states_today %>%  select(state, abb, naive_CFR, cases, deaths) # select data
cv_CFR$state_name <- cv_CFR$state
cv_CFR$state <- cv_CFR$abb
cv_CFR$abb <- NULL
# Create hover text
cv_CFR$hover <- with(cv_CFR, paste(state_name, '<br>', "CFR: ", naive_CFR, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths))
# Set up mapping details
set_map_details <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)
# Create the map
fig <- plot_geo(cv_CFR, locationmode = 'USA-states') %>% 
  add_trace(
    z = ~naive_CFR, text = ~hover, locations = ~state,
    color = ~naive_CFR, colors = 'Purples'
  )
fig <- fig %>% colorbar(title = "CFR May 1 2020", color = c(0,shadeLimit))
fig <- fig %>% layout(
    title = paste('CFR by State as of', Sys.Date(), '<br>(Hover for value)'),
    geo = set_map_details
  )
fig_Today <- fig
### Plot side by side

FINISH THE CODE HERE

subplot( ___ ) ```

Lab-11

Amei Hao

10/28/2020

Learning Goals

Lab Description

Steps

I. Reading and processing the New York Times (NYT) state-level COVID-19 data

1. Read in the data

2. Look at the data

3. Format the data

4. Add `new_cases` and `new_deaths` and correct outliers

5. Add additional variables

II. Interactive plots

6. Explore scatterplots using `plot_ly()`

7. Explore scatterplot trend interactively using `ggplotly()` and `geom_smooth()`

8. Multiple line chart

9. Heatmaps

10. Map

FINISH THE CODE HERE

Lab-11

Amei Hao

10/28/2020

Learning Goals

Lab Description

Steps

I. Reading and processing the New York Times (NYT) state-level COVID-19 data

1. Read in the data

2. Look at the data

3. Format the data

4. Add new_cases and new_deaths and correct outliers

5. Add additional variables

II. Interactive plots

6. Explore scatterplots using plot_ly()

7. Explore scatterplot trend interactively using ggplotly() and geom_smooth()

8. Multiple line chart

9. Heatmaps

10. Map

FINISH THE CODE HERE

4. Add `new_cases` and `new_deaths` and correct outliers

6. Explore scatterplots using `plot_ly()`

7. Explore scatterplot trend interactively using `ggplotly()` and `geom_smooth()`